# Multi-task Neural Networks for Personalized Pain Recognition from Physiological Signals

arXiv: https://arxiv.org/abs/1708.08755 · HTML: https://mlsanity.com/paper/1708.08755/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2017.

## Built on by (most cited first, 11 of 11)

- [Transformer Encoder with Multiscale Deep Learning for Pain Classification Using Physiological Signals](https://mlsanity.com/paper/2303.06845/) (2023)
- [Joint Multimodal Transformer for Emotion Recognition in the Wild](https://mlsanity.com/paper/2403.10488/) (2024)
- [Multi-task Neural Networks for Pain Intensity Estimation using Electrocardiogram and Demographic Factors](https://mlsanity.com/paper/2407.19475/) (2024)
- [Non-contact Pain Recognition from Video Sequences with Remote Physiological Measurements Prediction](https://mlsanity.com/paper/2105.08822/) (2021)
- [Beyond One-Size-Fits-All: A Survey of Personalized Affective Computing in Human-Agent Interaction](https://mlsanity.com/paper/2304.00377/) (2023)
- [Uncovering Bias in Personal Informatics](https://mlsanity.com/paper/2303.15592/) (2023)
- [Automatic pain recognition from Blood Volume Pulse (BVP) signal using machine learning techniques](https://mlsanity.com/paper/2303.10607/) (2023)
- [Pain Forecasting using Self-supervised Learning and Patient Phenotyping: An attempt to prevent Opioid Addiction](https://mlsanity.com/paper/2310.06075/) (2023)
- [Uncertainty Quantification in Neural-Network Based Pain Intensity Estimation](https://mlsanity.com/paper/2311.08569/) (2023)
- [Pain Analysis using Adaptive Hierarchical Spatiotemporal Dynamic Imaging](https://mlsanity.com/paper/2312.06920/) (2023)
- [Learning from Stochastic Teacher Representations Using Student-Guided Knowledge Distillation](https://mlsanity.com/paper/2504.14307/) (2025)

## Abstract (arXiv)

Pain is a complex and subjective experience that poses a number of measurement challenges. While self-report by the patient is viewed as the gold standard of pain assessment, this approach fails when patients cannot verbally communicate pain intensity or lack normal mental abilities. Here, we present a pain intensity measurement method based on physiological signals. Specifically, we implement a multi-task learning approach based on neural networks that accounts for individual differences in pain responses while still leveraging data from across the population. We test our method in a dataset containing multi-modal physiological responses to nociceptive pain.